U.S. patent application number 12/585598 was filed with the patent office on 2010-08-19 for salivary metabolic biomarkers for human oral cancer detection.
This patent application is currently assigned to THE REGENTS OF THE UNIVERSITY OF CALIFORNIA. Invention is credited to Akiyoshi Hirayama, Tomoyoshi Soga, Masahiro Sugimoto, Masaru Tomita, David T.W. Wong.
Application Number | 20100210023 12/585598 |
Document ID | / |
Family ID | 42039931 |
Filed Date | 2010-08-19 |
United States Patent
Application |
20100210023 |
Kind Code |
A1 |
Wong; David T.W. ; et
al. |
August 19, 2010 |
Salivary metabolic biomarkers for human oral cancer detection
Abstract
The present invention provides a novel oral cancer and
periodontal disease salivary metabolome for use in the diagnosis or
for providing a prognosis for oral cancer and periodontal disease
in an individual. The present invention also provides novel methods
of diagnosing or providing a prognosis for oral cancer or
periodontal disease by detecting metabolites found in the saliva of
an individual. Finally, the present invention provides kits for the
detection of salivary metabolites useful in the diagnosis or
prognosis of oral cancer and periodontal disease in an
individual.
Inventors: |
Wong; David T.W.; (Beverly
Hills, CA) ; Tomita; Masaru; (Tsuruoka, JP) ;
Sugimoto; Masahiro; (Tsuruoka, JP) ; Hirayama;
Akiyoshi; (Tsuruoka, JP) ; Soga; Tomoyoshi;
(Tsuruoka, JP) |
Correspondence
Address: |
OLIFF & BERRIDGE, PLC
P.O. BOX 320850
ALEXANDRIA
VA
22320-4850
US
|
Assignee: |
THE REGENTS OF THE UNIVERSITY OF
CALIFORNIA
OAKLAND
CA
KEIO UNIVERSITY
TOKYO
|
Family ID: |
42039931 |
Appl. No.: |
12/585598 |
Filed: |
September 18, 2009 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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61099110 |
Sep 22, 2008 |
|
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|
Current U.S.
Class: |
436/90 ; 204/451;
205/792; 436/106; 436/111; 436/127; 436/96 |
Current CPC
Class: |
G01N 33/57407 20130101;
G01N 2800/18 20130101; G01N 33/57488 20130101; Y10T 436/17
20150115; Y10T 436/20 20150115; Y10T 436/173845 20150115; Y10T
436/145555 20150115 |
Class at
Publication: |
436/90 ; 436/106;
436/127; 436/111; 436/96; 205/792; 204/451 |
International
Class: |
G01N 33/68 20060101
G01N033/68; G01N 33/00 20060101 G01N033/00; G01N 27/26 20060101
G01N027/26; B01D 57/02 20060101 B01D057/02 |
Goverment Interests
STATEMENT AS TO RIGHTS TO INVENTIONS MADE UNDER FEDERALLY SPONSORED
RESEARCH AND DEVELOPMENT
[0002] This invention was made with Government support under NIH
Grant No. RO1 DE 015970. The Government has certain rights in this
invention.
Claims
1. A method of diagnosing or providing a prognosis for an oral
disease in an individual, the method comprising the steps of: (a)
determining the level in a salivary sample from the individual of
at least one salivary oral disease metabolite biomarker selected
from those found in Table 2; and (b) determining if the level of
said at least one metabolite corresponds to a level found in an
oral disease metabolome profile, thereby diagnosing or providing a
prognosis for oral disease.
2. The method of claim 1, wherein said oral disease is oral cancer
or periodontal disease.
3. The method of claim 1, wherein the level of said at least one
salivary oral disease metabolite biomarker is determined by using a
technique selected from the group comprising HPLC, TLC,
electrochemical analysis, capillary electrophoresis, mass
spectrometry, refractive index spectroscopy (RI), Ultra-Violet
spectroscopy (UV), fluorescent analysis, gas chromatography (GC),
radiochemical analysis, Near-InfraRed spectroscopy (Near-IR),
Nuclear Magnetic Resonance spectroscopy (NMR), and light scattering
analysis (LS).
4. The method of claim 1, wherein step (b) comprises the sub-steps
of: comparing the level of said at least one metabolite to a first
profile comprising an oral disease metabolic profile; (ii)
comparing the level of said at least one metabolite to a second
profile comprising a control metabolic profile; and (iii)
determining which metabolic profile is most similar to the level of
said at least one metabolite from said individual, thereby
diagnosing said individual as either having or not having an oral
disease.
5. The method of claim 4, further comprising the step of comparing
the level of said at least one metabolite to a third profile
comprising a periodontal disease metabolic profile.
6. A method of identifying a salivary oral disease metabolite
biomarker, the method comprising the steps of: (a) determining the
level of a metabolite in a first saliva sample from an individual
with oral disease; (b) comparing the level of said biomarker to the
level of the same biomarker in a second saliva sample from an
individual without oral disease; and (c) determining if the level
of the metabolite in said first sample is different from the level
of the metabolite in said second sample, thereby identifying a
salivary oral disease metabolite biomarker.
7. A kit for use in diagnosing or providing a prognosis for oral
disease in an individual, the kit comprising at least one reagent
for detecting a metabolite selected from those found in Table 2.
Description
CROSS-REFERENCES TO RELATED APPLICATIONS
[0001] This application claims the benefit of priority to U.S.
provisional application U.S. 61/099,110, filed on Sep. 22, 2008,
which is incorporated herein by reference.
REFERENCE TO A "SEQUENCE LISTING," A TABLE, OR A COMPUTER PROGRAM
LISTING APPENDIX SUBMITTED ON A COMPACT DISK
[0003] Not Applicable
BACKGROUND OF THE INVENTION
[0004] Oral cancer, predominantly oral squamous cell carcinoma
(OSCC), is a high impact disease in the oral cavity, affecting more
than 34,000 people in the United States each year (American Cancer
Society, 2007) and more than 400,000 people annually worldwide (The
Oral Cancer Foundation www.oralcancerfoundation.org). Despite
treatment advances, the disease's overall 5-years survival rate is
only about 50% and has not improved in the past 30 years, remaining
among the worst of all cancers (Epstein, J. B. et al., J Can Dent
Assoc, 68(10):617-21 (2002); Mao, L. et al., Cancer Cell,
5(4):311-16 (2004)). The death rate associated with this cancer is
particularly high not because it is hard to discover or diagnose,
but due to the cancer being routinely discovered late, after
metastasis has already spread to the lymph nodes or the neck (The
Oral Cancer Foundation www.oralcancerfoundation.org). Thus, a
novel, non-obtrusive diagnostic tool that is inexpensive, highly
sensitive, and accurate, is necessary to detect oral cancer early
as possible in order to reduce the high mortality rate (Mignogna,
M. D. et al., Eur J Cancer Prev, 13(2):139-42 (2004)).
[0005] Oral cancer tumors arise through a series of molecular
mutations that lead to uncontrolled cellular growth from
hyperplasia to dysplasia to carcinoma in situ followed by invasive
carcinoma. Major risk factors include tobacco and alcohol
consumption along with environmental and genetics factors (Brinkman
and Wong, 2006; Figuerido et al., 2004; Hu et al., 2007; Turhani et
al., 2006). These cancers are usually detected at late stages when
the disease has advanced and therefore results in poor prognosis
and survival. Presently, surgery and radiotherapy are the primary
treatments, but due to the location in the head and neck; this
usually results in postoperative defects and functional impairments
in patients (Thomson and Wylie, 2002). Therefore, early disease
detection is imperative because it can result in a more effective
treatment with superior results.
[0006] Saliva, blood, and urine are systematically affected by
various pathways and therefore these biofluids are well suited for
use in monitoring systemic diseases and conditions. A number of
clinical tools have been developed to aid in cancer diagnosis and
prognosis through the detection of differential gene expression in
serum and urine (Drake, R. R. et al., Expert Rev Mol Diagn,
5(1):93-100 (2005); Hu, S. et al., Expert Rev Proteomics,
4(4):531-38 (2007)). Although saliva-based detection of early stage
oral cancer has been hampered in the past due to the low density of
saliva, low variety of associated genetic mutations, and various
effects caused by cancer progress, a number of studies have
explored the molecular level of markers that discriminate between
patients with oral cancer and healthy individuals. For example,
concentrations of Cyfra 21-1, tissue polypeptide antigen, CA125,
CA19-9, SCC, and carcinoembryonic, tumor markers of oral squamous
cell carcinoma (OSCC) in serum, have also been found to be elevated
in the saliva of OSCC patients (Nagler, R. et al., Clin Cancer Res,
12(13):3979-84 (2006)).
[0007] Saliva has gained notable attention as a diagnostic fluid
because of its simple collection and processing, minimal
invasiveness and low costs. Many researchers have studied salivary
proteins as potential diagnostic markers for various diseases such
as breast cancer, ovarian cancer, Sjogrens syndrome, hepatocellular
carcinoma, leukoplakia and oral cancer (Ryu et al., 2006; Streckfus
et al., 2000; Rhodus et al., 2005; Brailo et al., 2006, Yio et al.,
1992; Gorelik et al., 2005; Hu et al., 2007). The human salivary
proteome was recently completed and it was found that salivary
ductal fluids from the parotid, submandibular, and sublingual
glands contain roughly 1,166 proteins (Denny et al., J. Proteome
Res., 7(5):1994-2006 (2008)). Use of these proteins as potential
salivary disease markers may lead to the development of simple
clinical tools for early detection of numerous diseases as well as
for monitoring disease progression before, after, and during
treatment (Kingsmore, 2006).
[0008] Other examples of studies identifying salivary markers
include Xie et al., who identified over one thousand proteins in
the whole saliva of oral cancer patients. Their analysis revealed
that whole saliva contains exfoliated cells, the majority of which
are epithelial, as well as over 30 different bacterial species,
some of which putatively contribute to cancer development (Xie, H.
et al., Mol Cell Proteomics, 7(3):486-98 (2008)). Shpitzer et al.
analyzed inorganic compounds and proteins such as albumin, lactate
dehydrogenase, amylase, total immunoglobulin, etc. in the saliva of
oral squamous cell carcinoma (OSCC) patients. They found that
potassium and amylase levels were significantly reduced while the
level of other markers were increased in the saliva of oral cancer
patients as compared to control samples (Shpitzer, T., et al., J
Cancer Res Clin Oncol, 133(9):613-17 (2007)). Chen et al. found
that levels of both alpha-amylase and albumin were significantly
higher in the saliva of OSCC patients than in normal controls, as
measured by matrix-assisted laser desorption/ionization mass
spectrometry (MALDI-MS) (Chen, Y. C. et al., Rapid Commun Mass
Spectrom, 16(5):364-9 (2002)). Li et al. developed a transcriptome
diagnostic approach for oral cancer detection based on mRNA profile
pattern analysis. Classification and regression trees (CART) model
containing quantitative levels of RNA, OAZ, SAT, IL8, IL1b resulted
in a diagnostic method with greater than 90% sensitivity and
specificity for distinguishing patients with OSCC from the control
individuals (Li, Y. et al., Clin Cancer Res, 10(24):8442-50
(2004)). Finally, Hu et al. used MALDI-MS in a shotgun proteomics
approach to discover a panel of five highly discriminatory salivary
proteins that are found at significantly different levels in the
saliva specimen from OSCC patients as compared to controls (Hu, S.
et al., Cancer Genomics Proteomics, 4(2):55-64 (2007)) (Hu et al.,
2008).
[0009] Metabolics hold the potential for bridging the gap between
genotype and phenotype as well as for promoting comprehensive and
holistic understanding of a cell. Metabolic methodologies enable
simultaneous monitoring of many hundreds of metabolites in both
qualitative and quantitative fashion and allow for the elucidation
of cellular pathway behaviors that result in response to specific
environmental variances (Fernie, A. R. et al., Nat Rev Mol Cell
Biol, 5(9):763-9 (2004); Fraenkel, D. G., Annu Rev Genet, 26:159-77
(1992); Ideker, T. et al., Science, 292(5518):929-34 (2001);
Raamsdonk, L. M. et al., Nat Biotechnol, 19(1):45-50 (2001);
Spinnler, H. E. et al., Proc Natl Acad Sci USA, 93(8):3373-6
(1996)). Large scale metabolite analysis have been performed using
gas chromatography coupled with mass spectrometry (Fiehn, O. et
al., Nat Biotechnol, 18(11):1157-61 (2000)), liquid chromatography
coupled with mass spectrometry (LC-MS) (Plumb, R. et al., Analyst,
128(7):819-23 (2003)), NMR (Reo, N. V., Drug Chem Toxicol,
25(4):375-82 (2002)), Fourier transform ion cyclotron resonance
mass spectrometry (Aharoni, A. et al., Omics, 6(3):217-34 (2002)),
and capillary electrophoresis coupled with mass spectrometry
(CE-MS) (Soga, T. et al., J Proteome Res, 2(5):488-94 (2003)). In
two studies, metabolite profiles have been elucidated from the
urine of renal cell carcinoma patients (Kind, T. et al., Anal
Biochem, 363(2):185-95 (2007); Perroud, B. et al., Mol Cancer, 5:64
(2006)). Metabolome analysis by NMR has also been implemented as a
diagnostic tool for the prognosis of lymph node metastasis and for
monitoring patient response to chemotherapy for breast cancer
(reviewed, Claudino, W. M. et al., J Clin Oncol, 25(19):2840-6
(2007)).
[0010] The present invention fulfills a need in the art for
salivary metabolic biomarkers useful for diagnosing and providing a
prognosis for oral cancers and periodontal disease. The present
invention also provides non-invasive methods for the diagnosis of
oral cancers and periodontal disease.
BRIEF SUMMARY OF THE INVENTION
[0011] The present invention provides for the first time salivary
metabolite biomarkers, identified from global metabolome profiling
analysis, for use in oral disease diagnosis and prognosis. Due to
the identification of this oral disease metabolome and the
advancements in high-throughput capillary electrophoresis-mass
spectrometry (CE-MS) techniques, the methods of the present
invention are well suited for use in the early detection of oral
diseases, including oral cancers and periodontal disease.
Furthermore, the methods of the present invention can be used as a
discovery platform for the identification of other salivary
metabolic biomarkers.
[0012] In one embodiment, the present invention provides salivary
metabolite biomarkers useful in the diagnosis and prognosis of oral
disease in a subject. The small molecule biomarkers found in Table
2 allow for the differentiation of salivary samples from
individuals with oral disease, and healthy individuals. In one
embodiment, the present invention provides salivary oral disease
metabolomes for use in the diagnosis and prognosis of oral disease
in a subject. In a specific embodiment, these metabolomes comprise
subsets of metabolites found in Table 2. In another embodiment, the
metabolomes of the present invention comprise all of the
metabolites found in Table 2.
[0013] In a second embodiment, the present invention provides
methods of diagnosing or providing a prognosis for oral disease in
a subject. In certain embodiments, these methods comprise the steps
of first determining the level of at least one salivary oral
disease metabolite biomarker in a sample of saliva from the
subject, then comparing said level to a salivary oral disease
metabolome, or both, and finally determining if the at least one
salivary oral disease metabolite is present at a differential level
in the salivary sample as compared to a level found in a sample
from an individual not suffering from oral disease, thereby
diagnosing or providing a prognosis for oral disease in the
subject. In other embodiments, the level of the at least one
salivary oral disease metabolite is compared to a control salivary
metabolome. In yet other embodiments, the at least one salivary
oral disease metabolites comprise metabolites selected from those
found in Table 2.
[0014] In a third embodiment, the present invention provides
methods of identifying novel salivary oral disease small molecule
biomarkers or oral disease metabolite biomarkers. In one
embodiment, the methods comprise the steps of first separating
metabolites from a salivary sample from one or more individuals
suffering from oral disease, then determining the level of said
metabolites, and finally determining if any of the metabolites are
present at a differential level in the saliva of at least one
individual suffering from oral disease as compared to the level of
said metabolite in a salivary sample from an individual not
suffering from oral disease, thereby identifying a novel salivary
oral disease small molecule biomarker.
[0015] In a fourth embodiment, the present invention provides
methods of developing a model for the classification of disease
states in an individual. In one embodiment, the method comprises
the steps of first determining the level of at least one metabolite
in a first sample of saliva from an individual or group of
individuals suffering from a first disease state, next determining
the level of said at least one metabolite in a second sample of
saliva from an individual or group of individual suffering from a
second disease state, and finally comparing the levels of said at
least one metabolite from said first and said second sample,
thereby developing a model for the classification of said first and
said second disease states.
[0016] In a fifth embodiment, the present invention provides a
method of classifying of differentiating a disease state in an
individual suspected of having one of two or more disease states.
In some embodiments, the method comprises the steps of determining
a salivary metabolic profile from an individual, comparing said
metabolic profile to a classification model of said two or more
disease states, and determining which disease state most highly
correlates with said metabolic profile, thereby classifying the
disease state of an individual.
[0017] In a sixth embodiment, the present invention provides kits
useful in the diagnosis and prognosis of oral disease in an
individual. In some embodiments, the kits of the present invention
comprise reagents that bind to at least one salivary oral disease
metabolite biomarker. In certain embodiments the salivary oral
disease biomarkers are those found in Table 2. In other
embodiments, the kits comprise a plurality of reagents that bind to
a subset of or all of the metabolites found in Table 2.
BRIEF DESCRIPTION OF THE DRAWINGS
[0018] FIG. 1. show a heatmap of 45 peaks showing differential
levels (p<0.05) in individuals with oral cancer, healthy control
individuals, and individuals with periodontal disease, generated
with the TM4 software package.
[0019] FIG. 2. shows a result of ROC curve analysis for the
discriminating ability of multiple salivary metabolites for A) oral
cancer (n=69) or B) periodontal diseases (n=11) between healthy
controls (n=87). Solid lines and dotted lines are ROC curves
obtained using whole data as training set and 10-fold cross
validation, respectively. Using a cutoff probability of 50%, the
calculated area under the ROC curves were 0.865 (0.810) for oral
cancer and 0.969 (0.954) for periodontal diseases, respectively.
NOTE: Non-parenthetic values are obtained by full-training data and
parenthetic values are obtained in tenfold cross-validation.
[0020] FIG. 3 shows score plots of principal components (PC)
analyses. The subjects in all groups are shown in 3-dimensional (a)
without three outliers in oral cancer, and its enlarged view (b).
The dots denote healthy controls (open circle), oral (open
rectangle), breast (christcross), pancreatic cancer (filled
circle), and periodontal disease (open triangle). The cumulative
proportions of the first, second and third PCs (PC1, PC2, and PC3)
were 44.8, 57.6 and 67.0%.
[0021] FIG. 4 shows score plots of principal components (PC)
analysis according to sex. The values for males and females are
visualized in open circle and filled rectangle colored,
respectively. Three-dimensional PC plots for healthy controls (a),
and for subjects with oral cancer (b) are shown. A total of 42 male
(blue) and 27 female (red) control subjects and 41 male and 23
female patients with oral cancer were included in this analysis.
Eighteen control subjects, 5 patients with oral cancer and all of
the patients with other diseases were excluded because of
unavailable sex information. The cumulative proportions of PC1,
PC2, and PC3 for the control subjects were 43.0%, 52.9% and 60.6%,
respectively, and those for patients with oral cancer were 50.2%,
65.7% and 77.5%, respectively.
[0022] FIG. 5. shows principal components (PC) analysis score plots
for race and ethnic groups. The data for African-American, Asian,
Caucasian and Hispanic subjects are visualized in open circle,
filled rectangle, cross joint, and filled circle, respectively.
Three-dimensional PC plots for healthy controls (a) and for
subjects with oral cancer (b) are shown. The race or ethnic groups
included 12 and 4 African-Americans, 15 and 5 Asians, 37 and 41
Caucasians, and 5 and 5 Hispanics as healthy controls and subjects
with oral cancer, respectively. Eighteen control subjects, 5
patients with oral cancer, and all of the patients with other
diseases were excluded because of unavailable race or ethnic
information. The cumulative proportions of PC1, PC2 and PC3 for the
control subjects were 43.0%, 52.9% and 60.9%, respectively, and
those for subjects with oral cancer were 42.0%, 63.2% and 70.2%,
respectively.
DETAILED DESCRIPTION OF THE INVENTION
[0023] Although transcriptomic (Zimmermann, B. G. et al., Ann N Y
Acad Sci, 1098:184-91 (2007); Zimmermann, B. G. and Wong, D. T.,
Oral Oncol, p. doi:10.1016/j.oraloncology.2007.09.009 (2007)) and
proteomic studies (Hu, S. et al., Proteomics, 5(6):1714-28 (2005))
of oral cancer have identified potential nucleic acid and protein
biomarkers for clinical application, global changes in the levels
of salivary metabolites from patients suffering from oral disease
have yet to be investigated via metabolic approaches. The present
invention provides novel CE-MS methods well-suited for metabolic
studies, particularly when high resolution compound separation and
high detection sensitivity are required (Soga, T. et al., J
Proteome Res, 2(5):488-94 (2003); Soga, T. et al., J Biol Chem,
281(24):16768-76 (2006)). Capillary electrophoresis (CE) enables
temporal separation of components based on charge and shape, while
mass spectroscopy (MS) provides additional secondary separation for
compounds that co-migrate in CE. Thus, CE-MS is particularly suited
for salivary metabolome analysis since saliva is known to contain
various electrolytes. In one embodiment, the present invention
provides small molecule biomarkers useful for the diagnosis and
prognosis of oral cancer and periodontal disease, that have been
identified by global metabolite profile analysis using CE-MS on
salivary samples from patients suffering from oral cancer or
periodontal disease. These profiles were compared to profiles
generated from salivary samples obtained from healthy control
individuals. In a second embodiment, the present invention also
provides methods that are complementary approaches for early
detection of oral cancers that may be used in conjunction with
proteome and transcriptome-based diagnostic tests.
[0024] A large-scale metabolic analysis was conducted to explore
the differences in the salivary metabolite profiles between healthy
individuals and those with oral cancer and periodontal disease.
CE-TOF-MS-based analysis identified 28 metabolites that were
present in the saliva of patients with oral disease at
statistically significant differential levels as compared to those
in healthy control individuals (Table 2).
[0025] In one embodiment, the present invention provides salivary
oral cancer metabolite biomarkers useful for diagnosing and
providing a prognosis of oral cancer in a subject. Useful
biomarkers include those that are present at a differential level
or concentration in the saliva of an individual or group of
individuals suffering from oral cancer as compared to the level or
concentration in the saliva of an individual or group of
individuals not suffering from oral cancer. In one embodiment, the
biomarkers comprise the metabolites found in Table 2. In a specific
embodiment, the biomarkers are selected from the group consisting
of C.sub.8H.sub.9N (120.0801 m/z), Threonine, Leucine, Isoleucine,
Cadaverine, Glutamic acid, Tyrosine, Piperideine,
alpha-Aminobutyric acid, Serine, Alanine, Valine, Phenylalanine,
Pipecolic acid, Choline, C.sub.4H.sub.9N (72.0813 m/z), Tryptophan,
C.sub.6H.sub.6N.sub.2O.sub.2 (139.05 m/z), Glutamine,
C.sub.5H.sub.14N.sub.5 (145.1331 m/z), beta-Alanine, Carnitine,
C.sub.6H.sub.8OS.sub.2 or C.sub.4H.sub.5N.sub.2O.sub.11P (288.9691
m/z), Piperidine, Pyrroline hydroxycarboxylic acid, Taurine, and
Betaine.
[0026] In a second embodiment, the present invention provides
salivary periodontal metabolite biomarkers useful for diagnosing or
providing a prognosis of periodontal disease in a patient. In one
embodiment, salivary periodontal disease biomarkers comprise
metabolites that are present at a different level or concentration
in the saliva of a patient or group of patients suffering from
periodontal disease as compared to the level in an individual or
group of individuals not suffering from periodontal disease. In
another embodiment, the biomarkers comprise those found in Table 2.
In a specific embodiment, the periodontal biomarkers of the present
invention are selected from the group consisting of
C.sub.2H.sub.6N.sub.2 (59.0616 m/z), C.sub.7H.sub.8O.sub.3S
(173.0285 m/z), C.sub.30H.sub.62N.sub.19O.sub.2S.sub.3 (409.2312
m/z), C.sub.8H.sub.9N (120.0801 m/z), Threonine, Leucine,
Isoleucine, Cadaverine, Putrescine, Tyrosine, Ethanolamine,
Piperideine, alpha-aminobutyric acid, Serine, Alanine, Valine,
Phenylalanine, Pipecolic acid, Taurine, Tryptophan,
Glycerophosphocholine, and gamma-Aminobutyric acid.
[0027] In a third embodiment, the present invention provides
metabolite biomarkers that are useful for distinguishing between
oral cancer and periodontal disease. In one embodiment, the
biomarkers comprise small molecules that are present at a different
level or concentration in a patient or group of patients suffering
from oral cancer as compared to the level or concentration of the
metabolite in a patient or group of patients suffering from
periodontal disease. In a certain embodiment, the biomarkers are
those found in Table 2. In a specific embodiment the biomarkers are
selected from the group consisting of C.sub.2H.sub.6N.sub.2
(59.0616 m/z), C.sub.7H.sub.8O.sub.3S (173.0285 m/z),
C.sub.8H.sub.9N (120.0801 m/z), Piperideine, Pipecolic acid,
C.sub.4H.sub.9N (72.0813 m/z), Taurine, Glycerophosphocholine, and
Pyrroline hydroxycarboxylic acid.
[0028] In another embodiment, the present invention provides
salivary oral cancer metabolomes useful for diagnosing or providing
a prognosis for oral cancer in a subject. In one embodiment, the
oral cancer metabolomes of the present invention comprise subsets
of the metabolites found in Table 2. In other embodiments, the
metabolomes comprise at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
15, 20, 25, 30, 35, 40 or all of the metabolites found in Table 2.
In yet other embodiments, the metabolomes comprise a group of
metabolites that are present at a differential level or
concentration in the saliva of an individual or group of
individuals suffering from oral cancer as compared to the level or
concentration in the saliva of an individual or group of
individuals not suffering from oral cancer. In certain embodiments,
the metabolomes of the present invention are specific for a type of
oral cancer, such as oral squamous cell carcinoma, lip cancer,
tongue cancer, gingival carcinoma, buccal mucosal carcinoma, a head
and neck squamous cell carcinoma, and the like. In yet other
embodiments, the invention provides salivary metabolomes that
correspond to a particular stage or classification of oral cancer,
a particular prognosis for oral cancer, a particular prognosis for
the course of a treatment for oral cancer, or for a particular
prognosis for the efficacy or response of a particular
chemotherapeutic drug.
[0029] In a related embodiment, the present invention provides
salivary periodontal disease metabolomes useful for diagnosing or
providing a prognosis for periodontal disease in an individual. In
one embodiment, the oral cancer metabolomes of the present
invention comprise subsets of the metabolites found in Table 2. In
other embodiments, the metabolomes comprise at least about 1, 2, 3,
4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40 or all of the
metabolites found in Table 2. In yet other embodiments, the
metabolomes comprise a group of metabolites that are present at a
differential level or concentration in the saliva of an individual
or group of individuals suffering from periodontal disease as
compared to the level or concentration in the saliva of an
individual or group of individuals not suffering from periodontal
disease. In certain embodiments, the metabolomes of the present
invention are specific for a type of periodontal disease, such as
gingivitis, gingival periodontitis, cementum periodontitis,
alveolar periodontitis, connective tissue periodontitis, and the
like. In yet other embodiments, the invention provides salivary
metabolomes that correspond to a particular stage or classification
of periodontal disease, a particular prognosis for periodontal
disease, a particular prognosis for the course of a treatment for
periodontal disease, or for a particular prognosis for the efficacy
or response of a particular drug used to treat periodontal
disease.
[0030] In yet another embodiment, the metabolomes provided by the
invention are useful for classifying, distinguishing, or
differentiating between an oral cancer and a non-oral cancer, an
oral cancer and periodontal disease, or periodontal disease and a
non-oral cancer. In specific embodiments, the non-oral cancer may
be breast cancer or pancreatic cancer. In further embodiments, the
metabolomes of the present invention are useful in the development
of classification or differentiation models.
[0031] In one embodiment of the present invention, methods of
identifying novel salivary oral cancer metabolite biomarkers are
provided. In a specific embodiment, the methods comprise the steps
of: (a) determining the level of a metabolite in a first saliva
sample from an individual or group of individuals with oral cancer;
(b) comparing the level of said biomarker to the level of the same
biomarker in a second saliva sample from an individual or group of
individuals without oral cancer; and (c) determining if the level
of the metabolite in said first sample is different from the level
of the metabolite in said second sample, thereby identifying a
salivary oral cancer metabolite biomarker. In another embodiment,
the present invention provides methods of identifying novel
salivary periodontal disease metabolite biomarkers.
[0032] In another embodiment, the present invention provides
methods of diagnosing or providing a prognosis for oral cancer in
an individual. In a specific embodiment, the methods comprise the
steps of: (a) determining the level in a salivary sample from the
individual of at least one salivary oral cancer metabolite
biomarker selected from those found in Table 2; and (b) determining
if the level of said at least one metabolite corresponds to a level
found in an oral cancer metabolome profile, thereby diagnosing or
providing a prognosis for oral cancer. In particular embodiments,
methods of diagnosing or providing a prognosis for oral cancer
comprise the steps of (i) comparing the level of said at least one
metabolite to a first profile comprising an oral cancer metabolic
profile; (ii) comparing the level of said at least one metabolite
to a second profile comprising a control metabolic profile; and
(iii) determining which metabolic profile is most similar to the
level of said at least one metabolite from said individual, thereby
diagnosing said individual as either having or not having oral
cancer. In yet other embodiments, the methods further comprise the
step of comparing the level of at least one metabolite from an
individual to a periodontal disease metabolic profile.
[0033] In certain embodiments, the levels of oral cancer metabolite
biomarkers are determined by using a technique selected from the
group comprising HPLC, TLC, electrochemical analysis, capillary
electrophoresis, mass spectrometry, refractive index spectroscopy
(RI), Ultra-Violet spectroscopy (UV), fluorescent analysis, gas
chromatography (GC), radiochemical analysis, Near-InfraRed
spectroscopy (Near-IR), Nuclear Magnetic Resonance spectroscopy
(NMR), and light scattering analysis (LS). In a specific
embodiment, the salivary metabolites are detected by capillary
electrophoresis (CE) coupled with time of flight mass spectrometry
(TOF-MS). In other embodiments, the metabolites are detected by
tandem mass spectrometry (MS/MS).
[0034] In certain embodiments, the methods of the present invention
comprise the detection or determination of the level or
concentration of at least one salivary metabolite biomarker found
in Table 2. In other embodiments, the methods comprise the
detection at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25,
30, 35, 40, or all of the metabolites found in Table 2.
[0035] In some embodiments, the present invention provides methods
of classifying or differentiating a disease state in an individual.
In certain embodiments, the methods comprise the steps of: (a)
determining the level of at least one metabolite in a saliva sample
from said individual; (b) comparing the level of said at least one
metabolite to a first salivary metabolic profile for a first
disease state; (c) comparing the level of said at least one
metabolite to a second salivary metabolic profile for a second
disease state; and (d) determining which metabolic profile most
closely correlates with the level of said at least one metabolite,
thereby classifying the disease state in said individual.
[0036] In one embodiment, the present invention provides methods of
classifying or differentiating a specific type of oral cancer from
another type of oral cancer. In other embodiments, the methods
allow for classification or differentiation of oral cancer from
periodontal disease or oral cancer from a non-oral cancer. In yet
other embodiments, the methods provided herein allow for the
classification or differentiation of one stage of a specific
disease from a second stage of the same disease. For example, the
methods of the invention allow for the classification or
differentiation of a specific type of oral cancer at a first stage
from a second, more advanced stage, of the same type of oral
cancer, or of an oral cancer type with a first survival or
metastasis prognosis from an oral cancer of the same type with a
second survival or metastasis prognosis.
[0037] Many correlation methodologies may be employed for the
comparison of both individual metabolite levels and metabolome
profiles in the present invention. Non-limiting examples of these
correlation methods include parametric and non-parametric methods
as well as methodologies based on mutual information and non-linear
approaches. Examples of parametric approaches include without
limitation, Pearson correlation (or Pearson r, also referred to as
linear or product-moment correlation) and cosine correlation.
Non-limiting examples of non-parametric methods include Spearman's
R (or rank-order) correlation, Kendall's Tau correlation, and the
Gamma statistic. Each correlation methodology can be used to
determine the level of correlation between the levels of individual
metabolites in the data set. The correlation of the level of all
metabolites with all other metabolites is most readily considered
as a matrix. Using Pearson's correlation as a non-limiting example,
the correlation coefficient r in the method is used as the
indicator of the level of correlation. When other correlation
methods are used, the correlation coefficient analogous to r may be
used, along with the recognition of equivalent levels of
correlation corresponding to r being at or about 0.25 to being at
or about 0.5. The correlation coefficient may be selected as
desired to reduce the number of correlated gene sequences to
various numbers. In particular embodiments of the invention using
r, the selected coefficient value may be of about 0.25 or higher,
about 0.3 or higher, about 0.35 or higher, about 0.4 or higher,
about 0.45 or higher, or about 0.5 or higher. The selection of a
coefficient value means that where levels between metabolites in
the data set are correlated at that value or higher, they are
possibly not included in a subset of the invention. Thus in some
embodiments, the method comprises excluding or removing (not using
for classification) one or more metabolites that are present at a
level in correlation, above a desired correlation coefficient, with
another metabolite in the salivary data set. It is pointed out,
however, that there can be situations of metabolites that are not
correlated with any other metabolites, in which case they are not
necessarily removed from use in classification.
[0038] In yet other embodiments, the present invention provides
kits of the detection of salivary metabolites. In certain
embodiments, the kits are for detection of salivary oral cancer
metabolie biomarkers. In particular embodiments, the kits comprise
at least one reagent that binds to salivary metabolite found in
Table 2. In other embodiments, the kits comprise reagents that bind
to at least about 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35,
40, or all of the metabolites found in Table 2.
DEFINITIONS
[0039] "Metabolites" or "small molecules" include organic and
inorganic molecules which are present in a biological sample, such
as saliva or a cell. These include both products and intermediates
of metabolism as well as catabolism. The term does not include
large macromolecules, such as large proteins (e.g., proteins with
molecular weights over 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000
Da), large nucleic acids (e.g., nucleic acids with molecular
weights of over 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000 Da),
or large polysaccharides (e.g., polysaccharides with a molecular
weights of over 5,000, 6,000, 7,000, 8,000, 9,000, or 10,000 Da).
The small molecules are generally found free in solution, in the
cytoplasm of a cell, or in various organelles, such as the
mitochondria, where they form a pool of intermediates which can be
metabolized further or used to generate large molecules, called
macromolecules. The term "small molecules" includes signaling
molecules and intermediates in the chemical reactions that
transform energy derived from food into usable forms. Examples of
small molecules include sugars, fatty acids, amino acids, small
polypeptides, nucleotides, small polynucleotides, intermediates
formed during cellular processes, and other small molecules found
in biological samples. In one embodiment, the small molecules of
the invention are isolated.
[0040] The term "metabolome" may include all of the small molecules
present in a given organism, biological fluid, such as saliva,
biological sample, tissue, organ, cell, or subsets thereof. The
metabolome includes both metabolites as well as products of
catabolism. Metabolomes may also refer to subsets of small
molecules found in a biological sample from an organism suffering
from a disease, such as cancer or periodontal disease. In some
embodiments, a metabolome or a disease metabolome may refer to
subsets of small molecules from a biological sample, such as
saliva, from an individual suffering from a disease, which vary in
concentration as compared to those found in a similar biological
sample from an individual not suffering from the disease. General
methods for identifying a metabolome may be found, for example, is
U.S. Pat. No. 7,005,255.
[0041] "Disease metabolome" refers to a set of metabolites present
at different concentrations or levels in a biological sample from
an individual or group of individuals suffering from a given
disease. Disease metabolomes may be derived from a particular
biological sample, i.e. saliva, tissue, or tumor types. Metabolome
profiles may be generated from a single sample from an individual
or multiple samples from an individual, or alternatively from one
or more samples from a group of individuals. For example, a
salivary oral cancer metabolome profile may be generated from
samples of saliva taken from an individual or group of individuals
suffering from oral cancer. In one embodiment, a salivary oral
cancer metabolome profile of the present invention comprises levels
of one or more metabolites found in Table 2. In other embodiments,
a salivary oral cancer metabolome profile of the present invention
may comprise the level of at least about 2, 3, 4, 5, 6, 7, 8, 9,
10, 15, 20, 25, 30, or all of the metabolites found in Table 2.
[0042] "Metabolome profile", "metabolic profile", "disease
metabolome profile", or "disease metabolic profile" all refer to
the quantitative or qualitative level of metabolites found in a
metabolome, such as a control or salivary metabolome, or disease
metabolome, such as a salivary oral cancer metabolome or
periodontal disease metabolome.
[0043] In one embodiment, the invention pertains to a small
molecule profile of the entire metabolome of a species. In another
embodiment, the invention relates to a disease metabolome or a
subset thereof. In a particular embodiment, the invention relates
to a salivary disease metabolome, such as a cancer metabolome or a
periodontal metabolome. In other embodiments, the invention
pertains to a salivary metabolome from a systemic or genetically
predisposed disease. In another embodiment, the invention pertains
to a computer database (as described below) of a metabolome from a
species, e.g., an animal, e.g., a mammal, e.g., a mouse, rat,
rabbit, pig, cow, horse, dog, cat, bear, monkey, or human. In
another embodiment, the invention pertains to a small molecule
library comprising a metabolome or a subset thereof from an
organism, e.g., a mammal, e.g., a mouse, rat, rabbit, pig, cow,
horse, dog, cat, bear, monkey, and, preferably, or human.
[0044] A "small molecule profile," "metabolite profile," or
"metabolome profile" refers to information regarding the
concentration level of one or more small molecules or metabolites.
In some embodiments, the profiles of the present invention pertain
to a disease metabolome or subset thereof, i.e. oral cancer
metabolome, periodontal disease metabolome, systemic disease
metabolome, or genetically predisposed disease metabolome, from a
biological sample, i.e. saliva, blood, urine, biopsy, or tissue.
The profiles of the present invention may be derived from a sample
taken from a single individual, or alternatively from a cohort of
individuals suffering from a disease.
[0045] The term "cancer" refers to human cancers and carcinomas,
sarcomas, adenocarcinomas, lymphomas, leukemias, solid and lymphoid
cancers, etc. Examples of different types of cancer include, but
are not limited to, oral cancers, oral squamous cell carcinoma
(OSCC), breast cancer, gastric cancer, bladder cancer, ovarian
cancer, thyroid cancer, lung cancer, prostate cancer, uterine
cancer, testicular cancer, neuroblastoma, squamous cell carcinoma
of the head, neck, cervix and vagina, multiple myeloma, soft tissue
and osteogenic sarcoma, colorectal cancer, liver cancer (i.e.,
hepatocarcinoma), renal cancer (i.e., renal cell carcinoma),
pleural cancer, pancreatic cancer, cervical cancer, anal cancer,
bile duct cancer, gastrointestinal carcinoid tumors, esophageal
cancer, gall bladder cancer, small intestine cancer, cancer of the
central nervous system, skin cancer, choriocarcinoma; osteogenic
sarcoma, fibrosarcoma, glioma, melanoma, B-cell lymphoma,
non-Hodgkin's lymphoma, Burkitt's lymphoma, Small Cell lymphoma,
Large Cell lymphoma, monocytic leukemia, myelogenous leukemia,
acute lymphocytic leukemia, and acute myelocytic leukemia. Cancers
embraced in the current application include both metastatic and
non-metastatic cancers.
[0046] As used herein, "oral cancer" refers to a group of malignant
or neoplastic cancers originating in the head or neck of an
individual. Non-limiting examples of oral cancers include cancers
of the lip, tongue, throat, tonsils, neck, buccal vestibule, hard
or soft palate, gums (including gingival and alveolar carcinomas),
nasopharyngeal cancer, esophageal cancer, lingual cancer, buccal
mucosa carcinoma, head and neck squamous cell carcinoma, and the
like.
[0047] "Head and neck squamous cell carcinoma" refers to group of
cancers of epithelial cell origin originating in the head and neck,
including the oral cavity and pharynx. These tumors arise from
diverse anatomical locations, including the oral cavity,
oropharynx, hypopharynx, larynx, and nasopharynx, but in some cases
can have in common an etiological association with tobacco and/or
alcohol exposure. The oral cavity is defined as the area extending
from the vermilion border of the lips to a plane between the
junction of the hard and soft palate superiorly and the
circumvallate papillae of the tongue inferiorly. This region
includes the buccal mucosa, upper and lower alveolar ridges, floor
of the mouth, retromolar trigone, hard palate, and anterior two
thirds of the tongue. The lips are the most common site of
malignancy in the oral cavity and account for 12% of all head and
neck cancers, excluding nonmelanoma skin cancers. Squamous cell
carcinoma is the most common histologic type, with 98% involving
the lower lip. Next most common sites in order of frequency are the
tongue, floor of the mouth, mandibular gingiva, buccal mucosa, hard
palate, and maxillary gingiva. The pharynx consists of the
oropharynx, nasopharynx, and hypopharynx. The most common sites of
cancer in the oropharynx are the tonsillar fossa, soft palate, and
base of tongue, followed by the pharyngeal wall. The hypopharynx is
divided into the pyriform sinus (most common site of tumor
involvement), posterior pharyngeal wall, and postcricoid
region.
[0048] "Periodontal disease" refers to a group of diseases
affecting the gums of an individual, including gingivitis,
periodontitis, and the like. Periodontal diseases may be further
classified as aggressive, chronic, or necrotizing. Periodontitis is
generally characterized by inflammation of the periodontium
tissues, including the gingiva, the cementum, the alveolar bone,
and the periodontal ligaments.
[0049] "Therapeutic treatment" and "cancer therapies" refers to
chemotherapy, hormonal therapy, radiotherapy, and
immunotherapy.
[0050] By "therapeutically effective amount or dose" or "sufficient
amount or dose" herein is meant a dose that produces effects for
which it is administered. The exact dose will depend on the purpose
of the treatment, and will be ascertainable by one skilled in the
art using known techniques (see, e.g., Lieberman, Pharmaceutical
Dosage Forms (vols. 1-3, 1992); Lloyd, The Art, Science and
Technology of Pharmaceutical Compounding (1999); Pickar, Dosage
Calculations (1999); and Remington: The Science and Practice of
Pharmacy, 20th Edition, 2003, Gennaro, Ed., Lippincott, Williams
& Wilkins).
[0051] "Metastasis" refers to spread of a cancer from the primary
tumor or origin to other tissues and parts of the body, such as the
lymph nodes.
[0052] "Saliva" refers to any watery discharge from the mouth,
nose, or throat. For the purposes of this invention, saliva may
include sputum and nasal or post nasal mucous.
[0053] "Providing a prognosis" refers to providing a prediction of
the likelihood of metastasis, predictions of disease free and
overall survival, the probable course and outcome of cancer
therapy, or the likelihood of recovery from the cancer, in a
subject.
[0054] "Diagnosis" refers to identification of a disease state,
such as cancer or periodontal disease, in a subject. The methods of
diagnosis provided by the present invention can be combined with
other methods of diagnosis well known in the art. Non-limiting
examples of other methods of diagnosis include, detection of known
disease biomarkers in saliva samples, oral radiography, co-axial
tomography (CAT) scans, positron emission tomography (PE T),
radionuclide scanning, oral biopsy, and the like.
[0055] The terms "cancer-associated metabolite," or "tumor-specific
metabolite," or "biomarker," or "metabolite biomarker,"
interchangeably refer to a small molecule that is present in a
biological sample, e.g. saliva, from a subject with a disease, such
as cancer, periodontal disease, a systemic disease, or a
genetically predisposed disease, at a different level or
concentration in comparison to a biological sample from a subject
without the disease, and which is useful for the diagnosis of the
disease, for providing a prognosis, or for preferential targeting
of a pharmacological agent to an affected cell or tissue.
[0056] It will be understood by the skilled artisan that markers
may be used singly or in combination with other markers for any of
the uses, e.g., diagnosis or prognosis of a disease such as oral
cancer or periodontal disease.
[0057] "Biological sample" includes sections of tissues such as
biopsy and autopsy samples, and frozen sections taken for
histologic purposes. Such samples include saliva, blood and blood
fractions or products (e.g., serum, plasma, platelets, red blood
cells, and the like), lymph and tongue tissue, cultured cells,
e.g., primary cultures, explants, and transformed cells, stool,
urine, etc. A biological sample is typically obtained from a
eukaryotic organism, most preferably a mammal such as a primate
e.g., chimpanzee or human; cow; dog; cat; a rodent, e.g., guinea
pig, rat, mouse; rabbit; or a bird; reptile; or fish.
[0058] "Nucleic acid" refers to deoxyribonucleotides or
ribonucleotides and polymers thereof in either single- or
double-stranded form, and complements thereof. The term encompasses
nucleic acids containing known nucleotide analogs or modified
backbone residues or linkages, which are synthetic, naturally
occurring, and non-naturally occurring, which have similar binding
properties as the reference nucleic acid, and which are metabolized
in a manner similar to the reference nucleotides. Examples of such
analogs include, without limitation, phosphorothioates,
phosphoramidates, methyl phosphonates, chiral-methylphosphonates,
2-O-methyl ribonucleotides, peptide-nucleic acids (PNAs).
[0059] The terms "polypeptide," "peptide" and "protein" are used
interchangeably herein to refer to a polymer of amino acid
residues. The terms apply to amino acid polymers in which one or
more amino acid residue is an artificial chemical mimetic of a
corresponding naturally occurring amino acid, as well as to
naturally occurring amino acid polymers and non-naturally occurring
amino acid polymer.
[0060] The term "amino acid" refers to naturally occurring and
synthetic amino acids, as well as amino acid analogs and amino acid
mimetics that function in a manner similar to the naturally
occurring amino acids. Naturally occurring amino acids are those
encoded by the genetic code, as well as those amino acids that are
later modified, e.g., hydroxyproline, .gamma.-carboxyglutamate, and
O-phosphoserine. Amino acid analogs refers to compounds that have
the same basic chemical structure as a naturally occurring amino
acid, i.e., an .alpha.-carbon that is bound to a hydrogen, a
carboxyl group, an amino group, and an R group, e.g., homoserine,
norleucine, methionine sulfoxide, methionine methyl sulfonium. Such
analogs have modified R groups (e.g., norleucine) or modified
peptide backbones, but retain the same basic chemical structure as
a naturally occurring amino acid. Amino acid mimetics refers to
chemical compounds that have a structure that is different from the
general chemical structure of an amino acid, but that functions in
a manner similar to a naturally occurring amino acid.
[0061] Amino acids may be referred to herein by either their
commonly known three letter symbols or by the one-letter symbols
recommended by the IUPAC-IUB Biochemical Nomenclature Commission.
Nucleotides, likewise, may be referred to by their commonly
accepted single-letter codes.
[0062] A "label" or a "detectable moiety" is a composition
detectable by spectroscopic, photochemical, biochemical,
immunochemical, chemical, or other physical means. For example,
useful labels include .sup.32P, fluorescent dyes, electron-dense
reagents, enzymes (e.g., as commonly used in an ELISA), biotin,
digoxigenin, or haptens and proteins which can be made detectable,
e.g., by incorporating a radiolabel into the peptide or used to
detect antibodies specifically reactive with the peptide.
[0063] "Antibody" refers to a polypeptide comprising a framework
region from an immunoglobulin gene or fragments thereof that
specifically binds and recognizes an antigen. The recognized
immunoglobulin genes include the kappa, lambda, alpha, gamma,
delta, epsilon, and mu constant region genes, as well as the myriad
immunoglobulin variable region genes. Light chains are classified
as either kappa or lambda. Heavy chains are classified as gamma,
mu, alpha, delta, or epsilon, which in turn define the
immunoglobulin classes, IgG, IgM, IgA, IgD and IgE, respectively.
Typically, the antigen-binding region of an antibody will be most
critical in specificity and affinity of binding. Antibodies can be
polyclonal or monoclonal, derived from serum, a hybridoma or
recombinantly cloned, and can also be chimeric, primatized, or
humanized.
Diagnostic and Prognostic Methods
[0064] The present invention provides methods of diagnosing an oral
cancer or periodontal disease by examining metabolites or small
molecules that are present at differential levels or concentrations
in saliva. Diagnosis involves determining the level of one or more
metabolites of the invention in a patient and then comparing the
level to a baseline or range. Typically, the baseline value is
representative of a metabolite of the invention in a healthy person
or a individual not suffering from the disease of interest, as
measured using a biological sample such as saliva or a tissue
sample (e.g., tongue or lymph tissue), serum, or blood. Variation
of levels of a metabolite of the invention from the baseline range
(either up or down) indicates that the patient has the disease or
is at risk of developing the disease or a metastatic form thereof,
or extracapsular spread.
[0065] A person of ordinarily skill in the art will be able to
determine the appropriate metabolite profile for the methods of the
present invention by comparing small molecule or metabolite
profiles from diseased subjects with healthy or control
individuals. These comparisons can be manual, e.g., visually, or
can be made using software designed to make such comparisons, e.g.,
a software program may provide a secondary output which provides
useful information to a user. For example, a software program can
be used to confirm a profile or can be used to provide a readout
when a manual comparison between profiles is not possible. The
selection of an appropriate software program, e.g., a pattern
recognition software program, is within the ordinary skill of the
art. An example of such a program is Pirouette. It should be noted
that the comparison of the profiles can be done both quantitatively
and qualitatively.
[0066] Metabolite profiles can be determined by many methods well
known in the art. In one embodiment, a small molecule profile may
be determined by using capillary electrophoresis (CE) and mass
spectrometry (MS) (Garcia et al., Curr Opin Microbial.,
11(3):233-39 (2008)), HPLC (Kristal, et al., Anal. Biochem.,
263:18-25 (1998)), thin layer chromatography (TLC), or
electrochemical separation techniques (see, WO 99/27361, WO
92/13273, U.S. Pat. Nos. 5,290,420, 5,284,567, 5,104,639,
4,863,873, and U.S. Pat. No. RE32,920). Other techniques for
determining the presence of small molecules or determining the
identity of small molecules of the cell are also included, such as
refractive index spectroscopy (RI), Ultra-Violet spectroscopy (UV),
fluorescent analysis, radiochemical analysis, Near-InfraRed
spectroscopy (Near-IR), Nuclear Magnetic Resonance spectroscopy
(NMR), Light Scattering analysis (LS), time of flight mass
spectrometry (TOF-MS) and other methods known in the art.
[0067] A detectable moiety can be used in the assays described
herein. A wide variety of detectable moieties can be used, with the
choice of label depending on the sensitivity required, ease of
conjugation with the antibody, stability requirements, and
available instrumentation and disposal provisions. Suitable
detectable moieties include, but are not limited to, radionuclides,
fluorescent dyes (e.g., fluorescein, fluorescein isothiocyanate
(FITC), Oregon Green.TM., rhodamine, Texas red, tetrarhodimine
isothiocynate (TRITC), Cy3, Cy5, etc.), fluorescent markers (e.g.,
green fluorescent protein (GFP), phycoerythrin, etc.), autoquenched
fluorescent compounds that are activated by tumor-associated
proteases, enzymes (e.g., luciferase, horseradish peroxidase,
alkaline phosphatase, etc.), nanoparticles, biotin, digoxigenin,
and the like.
[0068] Immunoassay techniques and protocols are generally described
in Price and Newman, "Principles and Practice of Immunoassay," 2nd
Edition, Grove's Dictionaries, 1997; and Gosling, "Immunoassays: A
Practical Approach," Oxford University Press, 2000. A variety of
immunoassay techniques, including competitive and non-competitive
immunoassays, can be used (see, e.g., Self et al., Curr. Opin.
Biotechnol., 7:60-65 (1996)). The term immunoassay encompasses
techniques including, without limitation, enzyme immunoassays (EIA)
such as enzyme multiplied immunoassay technique (EMIT),
enzyme-linked immunosorbent assay (ELISA), IgM antibody capture
ELISA (MAC ELISA), and microparticle enzyme immunoassay (MEIA);
capillary electrophoresis immunoassays (CEIA); radioimmunoassays
(RIA); immunoradiometric assays (IRMA); fluorescence polarization
immunoassays (FPIA); and chemiluminescence assays (CL). If desired,
such immunoassays can be automated. Immunoassays can also be used
in conjunction with laser induced fluorescence (see, e.g.,
Schmalzing et al., Electrophoresis, 18:2184-93 (1997); Bao, J.
Chromatogr. B. Biomed Sci., 699:463-80 (1997)). Liposome
immunoassays, such as flow-injection liposome immunoassays and
liposome immunosensors, are also suitable for use in the present
invention (see, e.g., Rongen et al., J Immunol. Methods,
204:105-133 (1997)). In addition, nephelometry assays, in which the
formation of protein/antibody complexes results in increased light
scatter that is converted to a peak rate signal as a function of
the marker concentration, are suitable for use in the methods of
the present invention. Nephelometry assays are commercially
available from Beckman Coulter (Brea, Calif.; Kit #449430) and can
be performed using a Behring Nephelometer Analyzer (Fink et al., J.
Clin. Chem. Clin. Biochem., 27:261-276 (1989)).
[0069] Specific immunological binding of the antibody to an epitope
can be detected directly or indirectly. Direct labels include
fluorescent or luminescent tags, metals, dyes, radionuclides, and
the like, attached to the antibody. An antibody labeled with
iodine-125 (.sup.125I) can be used. A chemiluminescence assay using
a chemiluminescent antibody specific for the epitope marker is
suitable for sensitive, non-radioactive detection of protein
levels. An antibody labeled with fluorochrome is also suitable.
Examples of fluorochromes include, without limitation, DAPI,
fluorescein, Hoechst 33258, R-phycocyanin, B-phycoerythrin,
R-phycoerythrin, rhodamine, Texas red, and lissamine. Indirect
labels include various enzymes well known in the art, such as
horseradish peroxidase (HRP), alkaline phosphatase (AP),
.beta.-galactosidase, urease, and the like. A
horseradish-peroxidase detection system can be used, for example,
with the chromogenic substrate tetramethylbenzidine (TMB), which
yields a soluble product in the presence of hydrogen peroxide that
is detectable at 450 nm. An alkaline phosphatase detection system
can be used with the chromogenic substrate p-nitrophenyl phosphate,
for example, which yields a soluble product readily detectable at
405 nm. Similarly, a .beta.-galactosidase detection system can be
used with the chromogenic substrate
o-nitrophenyl-.beta.-D-galactopyranoside (ONPG), which yields a
soluble product detectable at 410 nm. An urease detection system
can be used with a substrate such as urea-bromocresol purple (Sigma
Immunochemicals; St. Louis, Mo.).
[0070] A signal from the direct or indirect label can be analyzed,
for example, using a spectrophotometer to detect color from a
chromogenic substrate; a radiation counter to detect radiation such
as a gamma counter for detection of .sup.125I; or a fluorometer to
detect fluorescence in the presence of light of a certain
wavelength. For detection of enzyme-linked antibodies, a
quantitative analysis can be made using a spectrophotometer such as
an EMAX Microplate Reader (Molecular Devices; Menlo Park, Calif.)
in accordance with the manufacturer's instructions. If desired, the
assays of the present invention can be automated or performed
robotically, and the signal from multiple samples can be detected
simultaneously.
[0071] The antibodies can be immobilized onto a variety of solid
supports, such as polystyrene beads, magnetic or chromatographic
matrix particles, the surface of an assay plate (e.g., microtiter
wells), pieces of a solid substrate material or membrane (e.g.,
plastic, nylon, paper), and the like. An assay strip can be
prepared by coating the antibody or a plurality of antibodies in an
array on a solid support. This strip can then be dipped into the
test sample and processed quickly through washes and detection
steps to generate a measurable signal, such as a colored spot.
[0072] Useful physical formats comprise surfaces having a plurality
of discrete, addressable locations for the detection of a plurality
of different biomarkers. Such formats include microarrays, or
"metabolite chips" (see, e.g., Ng et al., J Cell Mol. Med.,
6:329-340 (2002)) and certain capillary devices (see, e.g., U.S.
Pat. No. 6,019,944). In these embodiments, each discrete surface
location may comprise a binding reagent to immobilize one or more
metabolite markers for detection at each location. Surfaces may
alternatively comprise one or more discrete particles (e.g.,
microparticles or nanoparticles) immobilized at discrete locations
of a surface, where the microparticles comprise binding reagents to
immobilize one or more metabolite markers for detection.
[0073] The analysis can be carried out in a variety of physical
formats. For example, the use of microtiter plates or automation
could be used to facilitate the processing of large numbers of test
samples. Alternatively, single sample formats could be developed to
facilitate diagnosis or prognosis in a timely fashion.
Compositions, Kits and Integrated Systems
[0074] The invention provides compositions, kits and integrated
systems for practicing the assays described herein using
metabolites of the invention, binding reagents specific for
metabolite biomarkers of the invention, etc.
[0075] The invention provides assay compositions for use in solid
phase assays; such compositions can include, for example, one or
more metabolite or one or more binding reagents for the metabolites
of the invention immobilized on a solid support, and a labeling
reagent. In each case, the assay compositions can also include
additional reagents that are desirable for binding. Modulators of
expression or activity of metabolites of the invention can also be
included in the assay compositions.
[0076] The invention also provides kits for carrying out the
diagnostic assays of the invention. The kits typically include one
or more probes that comprise binding reagents that specifically
bind to metabolites of the invention, and a label for detecting the
presence of the probe. The kits may include several binding
reagents specific for the metabolites of the invention.
[0077] Optical images viewed (and, optionally, recorded) by a
camera or other recording device (e.g., a photodiode and data
storage device) are optionally further processed in any of the
embodiments herein, e.g., by digitizing the image and storing and
analyzing the image on a computer. A variety of commercially
available peripheral equipment and software is available for
digitizing, storing and analyzing a digitized video or digitized
optical images.
[0078] One conventional system carries light from the specimen
field to a cooled charge-coupled device (CCD) camera, in common use
in the art. A CCD camera includes an array of picture elements
(pixels). The light from the specimen is imaged on the CCD.
Particular pixels corresponding to regions of the specimen are
sampled to obtain light intensity readings for each position.
Multiple pixels are processed in parallel to increase speed. The
apparatus and methods of the invention are easily used for viewing
any sample, e.g., by fluorescent or dark field microscopic
techniques.
EXAMPLES
Example 1
[0079] This example describes a large-scale metabolome analysis of
salivary samples from disease patients suffering from an oral
cancers (n=69) or periodontal disease (n=11) and 87 healthy control
individuals.
[0080] Salivary metabolites of oral cancer (n=69) and periodontal
diseases (n=11), and control (n=87) were analyzed by coupling
capillary electrophoresis with electrospray ionization
time-of-flight mass spectrometry (CE-TOF-MS). The biomarkers
identified in the present example can be used for diagnosing or
providing a prognosis for oral cancer.
[0081] Patient Selection
[0082] Salivary fluid from oral cancer patients and healthy control
individuals was obtained from subjects of Caucasian, Asian,
African-American, and Hispanic origins. The ethnicity, age, and the
period of time between the saliva collection and measurement for
the study cohort is summarized in Table 1. To show that the
metabolites of the present invention can discriminate for oral
cancer, and are not just cancer-specific, the levels of metabolites
that were significantly different between the saliva of oral cancer
patients and control individuals were also compared in patients
suffering from breast and pancreatic cancer.
TABLE-US-00001 TABLE 1 Clinical variables for individuals included
in the control and disease cohorts. Oral Breast Pancreatic
Periodontal Ethnicity Control cancer Cancer Cancer diseases
Caucasian 37 41 Asian 15 5 African-American 12 4 N/A Hispanic 5 5
Missing 18 14 (Total) 87 69 Age: Min-Max 20-75 34-87 29-77 11-87
23-76 (Ave.) (41.4) (60.7) (55.1) (64.9) (57.4) Missing 2 5 10 2
2
[0083] Saliva Collection
[0084] Subjects were asked to refrain from eating, drinking,
smoking or using oral hygiene products for at least 1 hour prior to
collection. Subjects were asked to rinse mouth with water. Five
minutes after oral rinse, subjects were asked to spit into a 50 cc
Falcon tube kept on ice, for example in a styrofoam coffee cup
filled with ice. Subjects were reminded not to cough up mucus.
Typically, five ml of un-stimulated saliva can be collected in 5-10
minutes. Saliva samples were then centrifuged at 2,600(.times.)g
for 15 min at 4.degree. C., and were spun another 20 min with the
occurrence of incomplete separation. The supernatant was divided
and transferred equally into 2 new tubes. Sample in one tube was
used for protein analysis and supernatant in the second tube for
RNA analysis. The cell pellet remained in the original tube, label
with the same sample ID. Samples were processed and frozen within
30 minutes from the time of collection and then sent to the
laboratory stored on dry ice.
[0085] Sample Preparation
[0086] Frozen samples were thawed and 27 .mu.l of each was removed
for analysis. 3 p. 1 of water containing 2 mM methionine sulfone
and 2 mM 3-Aminopyrrolidine was added to each of the control and
oral cancer salivary samples. A second set of salivary samples (17
controls, breast cancers, pancreatic cancers, and periodontosis
samples) were thawed and 24 .mu.l of each was used for analysis. 6
.mu.l of water containing 1 mM methionine sulfone and 1 mM
3-Aminopyrrolidine was added to each of these sample.
[0087] Metabolite Standards
[0088] All chemical standards obtained from commercial sources were
analytical or reagent grade and were prepared in Milli-Q
(Millipore, Bedford, Mass.) deionized water, 0.1 N HCl or 0.1 N
NaOH. Typical stock solutions were prepared at 1 mM, 10 mM, or 100
mM depending on the reagent. Working solutions were prepared just
prior to use by diluting into deionized water to obtain the
appropriate concentration.
[0089] Instrumentation
[0090] All CE-MS experiments were performed using an Agilent CE
capillary electrophoresis system (Agilent technologies, Waldbronn,
Germany), an Agilent G3250AA LC/MSD TOF system (Agilent
Technologies, Palo Alto, Calif.), an Agilent 1100 series binary
HPLC pump, the G1603A Agilent CE-MS adapter, and G1607A Agilent
CE-ESI-MS sprayer kit. Data acquisition was performed using the
G2201 AA Agilent Chemstation software for CE and the Analyst QS
software (v. 1.1) for TOF-MS.
[0091] CE-TOF-MS Analysis
[0092] Metabolite separations were performed in a fused-silica
capillary (50 .mu.m diameter, 100 cm total length) filled with 1M
formic acid as the electrolyte. Sample solutions were injected at a
pressure of 50 mbar for 3 sec, and 30 kV voltage was applied. The
capillary temperature was maintained at 20.degree. C. and the
sample tray was maintained below 5.degree. C. Sheath liquid,
consisting of a solution of methanol and water (50% v/v) containing
0.5 .mu.M reserpine, was delivered at 10 .mu.l/min. ESI-TOF-MS was
performed in the positive ion mode. The capillary voltage was set
to 4,000 V, and nitrogen gas (heater temperature 300.degree. C.)
was set at a flow rate of 10 psig. For the TOF-MS, the fragmentor,
skimmer, and OCT RFV voltages were set at 75 V, 50 V, 125 V
respectively. An automatic recalibration function was performed
using two reference masses of reference standards. The methanol
dimmer adduct ion ([2MeOH+H].sup.+, m/z 65.059706) and hexakis
phosphazene ([M+H].sup.+, m/z 622.028963) provided the lock mass
for exact mass measurements. Exact mass data were acquired at the
rate of 1.5 cycles/sec over a 50-1,000 m/z range.
[0093] Metabolite Identification
[0094] Standard errors in mass accuracy of the metabolite
measurement technique based on the normalization of standard mass
calibration by CE-TOF-MS used in this study were 10 ppm in the
range from 200 to 400 m/z and 30 ppm outside of this range (Soga,
T. et al., J Biol Chem, 281(24):16768-76 (2006)). Given that the MS
data alone, at this level of precision, does not allow for the
assignment of metabolites to unknown peaks (Kind, T. and O. Fiehn,
BMC Bioinformatics, 7:234 (2006)), salivary samples were either
spiked with candidate molecules prior to analysis or tandem mass
spectrometry (MS/MS) fragment spectrums were compared, if candidate
compounds were commercially available. The Alanine, beta-Alanine,
Aspartic acid, Betanine, Cadaverine, Carnitine, Citrulline,
D-alpha-aminobutyric acid, gamma-Aminobutyric acid, Glutamic acid,
Glutamine, Glycine, Hypoxanthine, Isoleucine, Leucine, Lysine,
Ornitine, Pipecolic acid, Piperidine, Phenylalanine, Proline,
Putrescine, Serine, Threonine, Tyrosine, Tryptophan, and Valine,
were identified by based on the matched m/z values and normalized
migration times of corresponding standard compounds. The
composition formulae are also confirmed by isotope distribution
pattern.
[0095] The other peaks were identified based on the m/z value and
the predicted migration time by Artificial Neural Networks (ANNs)
(Sugimoto, M. et al. Large-scale prediction of cationic metabolite
identity and migration time in capillary electrophoresis mass
spectrometry using artificial neural networks. Anal Chem 77 (1),
78-84 (2005)). The candidates compounds were obtained from Kyoto
Encyclopedia of Gene and Genomics (KEGG) database (Goto, S. et al.
LIGAND: database of chemical compounds and reactions in biological
pathways. Nucleic Acids Res 30 (1), 402-404 (2002)) and Human
Metabolome Database (HMDB) (Wishart, D. S. et al. HMDB: the Human
Metabolome Database. Nucleic Acids Res 35 (Database issue),
D521-526 (2007)). The theoretical and measured m/z, and measured
and predicted migration times for Choline were 104.1070 m/z and
104.1075 m/z (4.96 ppm) and 8.75 min (parenthetic values are
errors). and 7.78 min. (0.97 min.), respectively. For Ethanolamine,
those were 62.0600 m/z and 62.0601 m/z (1.21 ppm) and 8.16 min. and
7.78 min. (0.38 min.), respectively. For Piperideine, those were
84.0808 m/z and 84.0807 m/z (0.42 ppm) and 8.78 min. and 7.84 min.
(0.94 min.), respectively. For Pyrroline hydroxycarboxylic acid,
those were 130.0499 m/z and 130.0498 m/z (0.230 ppm) and 12.90 min.
and 13.09 min. (0.19 min.), respectively.
[0096] Most of the conditions were identical to those used in the
cationic metabolite analysis using CE-TOF-MS. Methanol-water (50%
v/v) containing 1 .mu.M reserpine was delivered as the sheath
liquid at 5 .mu.l/min. ESI-Q-TOF-MS was conducted in the positive
product ion scan mode; the ion spray voltage was set at 5,500V. Dry
air (GS1) was maintained at 10 psi. The declustering potential 1
and 2, and the collision energy voltage ware set at 60V, 15V, and
20V, respectively. Recalibration was manually performed with
reserpine ([M+H].sup./, m/z 609.2906) and its fragment ion ([M+H]+,
m/z 195.0652).
[0097] Data Analysis and Results
[0098] Raw data were analyzed with software performing
noise-filtering, baseline correction, peak detection, and
integration of peak area from sliced electropherogram. The width of
each electropherogram was fixed at 0.02 m/z. Similar software is
commercially available, such as Mass Hunter from Agilent
Technologies, or XCMS for LC-MS data (Smith, C. A. et al., Anal
Chem, 78(3):779-87 (2006)). Subsequently, accurate m/z values for
each peak were detected in time and calculated with Gaussian curve
fitting to the mass spectrum on the m/z domain peak. The alignment
of peaks in multiple measurements were performed with dynamic
programming techniques described in (Baran, R. et al., BMC
Bioinformatics, 7:530 (2006)) and generated overlaid
electropherograms of matched peaks were generated.
[0099] Statistically significant peaks (p<0.15) from the
salivary control and the oral cancer samples were aligned. All peak
areas were standardized to internal controls resulting in relative
areas for the normalization of signal intensities in order to avoid
injection volume bias and sensitivity variance of the detector
between multiple measurements. Peaks undetected within a threshold
S/N ratio of 2, were considered to have a peak area of 0. The
relative areas of the pancreatic cancer, breast cancer, periodontal
disease, and second control cohort salivary samples were multiplied
by a factor of 1.25/1.1 to account for the standardization of
sample concentration.
[0100] CE-TOF-MS identified an average of 3041 peaks for each
sample (Min. 1585, Max 8400, S.D. 1137). Isotopic compounds,
ringing, spikes, fragment ions, and adduct ions were then removed
and the peak data sets were compared across the sample profiles (87
control samples and 69 oral cancer samples). Peaks were aligned
according to m/z and migration time. 45 metabolites were identified
as showing significant differences in comparison with healthy
controls (p<0.05; Steel-dwass test). The obtained marker pool
for discriminating oral cancer and controls includes 28
metabolites; Pyrroline hydroxycarboxylic acid, Leucine with
Isoleucine, Alanine, Choline, Tryptophan, Valine, Threonine,
Pipecolic acid, Glutamic acid, Histidine, Taurine, Piperideine,
Carnitine and other 2 metabolites (p<0.001; Steel-dwass test),
and Piperidine, alpha-Aminobutyric acid, Phenylalanine and a
metabolite (p<0.01; Steel-dwass test), also Betaine, Serine,
Tyrosine, Glutamine, beta-Alanine, Cadaverine and other two
metabolites (p<0.05; Steel-dwass test). The detected markers for
the all cohorts are listed in Table 2.
TABLE-US-00002 TABLE 2 Identified salivary metabolites that
discriminate between salivary samples from healthy individuals and
patients with oral cancers control oral cancer oral cancer control
vs oral cancer vs vs vs periodontal vs pancreatic periodontal
Metabolite marker candidates HMDB.dagger. oral cancer desease
breast cancer cancer desease C.sub.2H.sub.6N.sub.2 0.260 3.46
.times. 10.sup.-4*** 1.51 .times. 10.sup.-8*** 2.34 .times.
10.sup.-6*** 6.26 .times. 10.sup.-6*** (59.0616 m/z)
C.sub.32H.sub.48O.sub.13 0.834 0.907 4.75 .times. 10.sup.-4***
0.137 0.444 (214.444 m/z) C.sub.3H.sub.7NO.sub.2 0.955 0.0242*
0.154 3.68 .times. 10.sup.-4*** 0.0662 (90.0552 m/z)
C.sub.4H.sub.12N.sub.5 0.686 0.0251* 0.323 0.00636** 0.469
(131.1174 m/z) C.sub.4H.sub.9NO.sub.2 0.620 0.779 1.00 0.00504**
0.995 (104.0705 m/z) Cadaverine HMDB02322 0.0422* 0.00100** 0.993
0.449 0.488 C.sub.30H.sub.62N.sub.19O.sub.2S.sub.3 0.367 0.0141*
0.0354* 0.0812 0.420 (409.2312 m/z) C.sub.18H.sub.34N.sub.6O.sub.6
0.891 0.997 0.143 0.0247* 0.914 (215.1269 m/z) alpha-Aminobutyric
acid HMDB00650 0.00256** 0.00796** 1.00 0.0543 0.811 Alanine
HMDB00161 2.45 .times. 10.sup.-4*** 0.00647** 0.956 0.0968 0.945
Putrescine HMDB01414 0.890 0.0227* 0.138 0.0399* 0.444
C.sub.5H.sub.14N.sub.5 0.0212* 0.0825 0.998 0.471 0.994 (145.1331
m/z) Piperidine 0.00119** 0.137 0.165 1.00 0.972 Taurine HMDB00251
3.57 .times. 10.sup.-5*** 6.70 .times. 10.sup.-5*** 6.82 .times.
10.sup.-10*** 5.82 .times. 10.sup.-7*** 1.10 .times. 10.sup.-6***
Piperideine 2.83 .times. 10.sup.-4*** 3.19 .times. 10.sup.-4***
2.79 .times. 10.sup.-6*** 0.00226** 2.91 .times. 10.sup.-5***
Pipecolic acid HMDB00070 1.87 .times. 10.sup.-4*** 0.00948** 3.47
.times. 10.sup.-4*** 0.175 1.97 .times. 10.sup.-4***
C.sub.4H.sub.9N 2.02 .times. 10.sup.-7*** 0.949 5.62 .times.
10.sup.-4*** 0.996 0.00766** (72.0813 m/z) C.sub.8H.sub.9N 2.64
.times. 10.sup.-5*** 0.0385* 1.02 .times. 10.sup.-8*** 0.0123* 2.94
.times. 10.sup.-4*** (120.0801 m/z) Pyrroline hydroxycarboxylic
acid HMDB01369 1.28 .times. 10.sup.-5*** 0.176 1.36 .times.
10.sup.-5*** 0.867 0.00267** Betaine HMDB00043 0.0162* 0.576
0.0183* 0.133 0.0668 C.sub.4H.sub.7N 0.101 0.502 0.0349* 0.853
0.0858 (70.0655 m/z) C.sub.6H.sub.6N.sub.2O.sub.2 0.00270** 0.127
0.948 0.0715 0.974 (139.05 m/z) Leucine + Isoleucine HMDB00687 +
1.56 .times. 10.sup.-5*** 0.00150** 1.00 7.44 .times. 10.sup.-4***
0.868 HMDB00172 Phenylalanine HMDB00159 0.00333** 0.0198* 1.00
0.00351** 0.961 Tyrosine HMDB00158 0.0253* 0.0279* 0.926 0.0286*
0.969 Histidine HMDB00177 6.87 .times. 10.sup.-4*** 0.246 0.805
0.698 0.997 Proline HMDB00162 0.968 0.0598 0.0299* 0.0171* 0.291
Lysine HMDB00182 0.0779 0.459 0.426 0.00513** 1.00 Glycine
HMDB00123 1.00 0.193 0.0753 0.0122* 0.352 Ethanolamine HMDB00149
0.684 0.00187** 0.597 2.34 .times. 10.sup.-4*** 0.132
gamma-Aminobutyric acid HMDB00112 0.833 0.0133* 0.897 0.00108**
0.367 Aspartic acid HMDB00191 0.287 0.403 0.416 4.37 .times.
10.sup.-5*** 0.980 Valine HMDB00883 7.31 .times. 10.sup.-5***
0.0138* 0.990 0.00325** 0.999 Tryptophan HMDB00929 6.13 .times.
10.sup.-5*** 0.0113* 0.229 0.0461* 1.00 beta-Alanine HMDB00056
0.0407* 0.268 0.842 0.156 0.999 Glutamic acid HMDB00148 4.95
.times. 10.sup.-4*** 0.0757 1.00 0.00312** 1.00 Threonine HMDB00167
1.18 .times. 10.sup.-4*** 1.80 .times. 10.sup.-4*** 1.00 3.08
.times. 10.sup.-4*** 0.0829 Serine HMDB00187 0.0197* 0.00699**
0.846 1.16 .times. 10.sup.-4*** 0.435 Glutamine HMDB00641 0.0327*
0.111 0.975 0.00228** 0.998 Choline HMDB00097 2.30 .times.
10.sup.-5*** 0.0580 0.0115* 0.871 0.983 Carnitine HMDB00062 7.60
.times. 10.sup.-4*** 0.996 0.247 0.652 0.670 Glycerophosphocholine
HMDB00086 0.287 0.0322* 7.05 .times. 10.sup.-6*** 7.33 .times.
10.sup.-4*** 0.0138* C.sub.7H.sub.8O.sub.3S 0.962 0.0154* 2.71
.times. 10.sup.-4*** 0.0176* 0.0307* (173.0285 m/z)
C.sub.4H.sub.5N.sub.2O.sub.11P 0.0421* 0.256 5.17 .times.
10.sup.-6*** 0.628 0.808 (288.9691 m/z) *p < 0.05, **p <
0.01, ***p < 0.001
[0101] The salivary metabolite profiles obtained by CE-TOF-MS are
intricate products affected by multiple pathways. Therefore, direct
relationships between our experimental profiles and specific
biological pathways are difficult to elucidate. Nevertheless,
connections can be made between several of the identified oral
cancer metabolite biomarkers and general cancer progression
principles.
[0102] To evaluate the ability to discriminate oral cancer and
periodontal disease from control samples with the identified
biomarkers, multiple logistic regression (MLR) models were
developed between the healthy cohort and each disease cohort
independently. Step-wise variable selection method (backward
procedure for eliminating the non-predictive peaks with threshold
p>0.10) was used constructing the predictive model. The built
models and the ones yielded by tenfold cross-validation procedure
showed excellent separation abilities where all ROC values were
more than 0.81 even though in cross validation result (FIG. 2 and
Table 3).
TABLE-US-00003 TABLE 3 Logistic regression models of metabolome
biomarkers for discriminating between control and oral cancer or
periodontal diseases. Coefficient Standard Lower Upper Disease
Metabolite value error P value 95% CI 95% CI Oral Intercept 2.55
0.55 <.0001 1.54 3.73 cancer Alanine -26.43 13.31 0.047 -53.84
-1.95 Choline -19.60 7.01 0.0052 -34.20 -6.49 Leucine + Isoleucine
-68.55 18.36 0.0002 -108.66 -36.72 Glutamic acid -22.04 9.67 0.0226
-42.63 -4.73 120.0801 m/z -199.28 48.18 <.0001 -303.69 -113.22
Phenylalanine 63.86 16.17 <.0001 35.11 98.99 alpha-Aminobutyric
acid 633.12 226.5 0.0052 212.09 1105.80 Serine 67.50 28.92 0.0196
15.69 128.95 Periodontal Intercept 2.87 0.93 0.0021 1.28 5.06
diseases Trimethylamine -178.46 57.63 0.0020 -325.59 -87.53
Piperideine 1276.4 713.65 0.074 311.48 3289.21 NOTE. CI: confidence
interval.
[0103] MLR models for periodontal disease yield high ROC values
with only three metabolic markers. A metabolite heat map generated
using the MeV TM4 software package (FIG. 1) revealed that levels of
discriminatory metabolite biomarkers were constantly lower in the
control and periodontal disease cohorts. Conversely oral cancers
constituted widely diverse profile samples compared to the other
groups, which help explain why the MLR models for oral cancer
require more parameters for achieving accurate classification. The
heterogeneous nature of oral cancer, including OSCC, oropharyngeal
cancer, tongue cancer, or neck cancer, may cause such different
profiles, which might deteriorate the separation capability of a
single classification model.
[0104] Principle component analysis based on each disease groups
showed no clear prominent metabolite marker that has an enough
ability to separate groups alone (FIG. 4). Although single
metabolite is not enough to distinguish diseases, the changes of
these marker candidates generally showed consistency with previous
studies in the view of their changes. Polyamine is known generally
correlated cell growth and proliferation (Casero, R. A., Jr. &
Marton, L. J., Nat Rev Drug Discov 6 (5), 373-390 (2007); Gerner,
E. W. & Meyskens, F. L., Jr. Nat Rev Cancer 4 (10), 781-792
(2004); Tabor, C. W. & Tabor, H. Annu Rev Biochem 53, 749-790
(1984)), also with tumour growth in oral cancer (Dimery, I. W. et
al., Am J Surg 154 (4), 429-433 (1987)). Putrescine is used to
monitor chemotherapy impact on oral cancer cells (Okamura, M. et
al. Anticancer Res 27 (5A), 3331-3337 (2007)). The concentration of
putrescine and cadaverine in serum decreased on radiotherapy in
cancer patients, but were still higher than in healthy persons
(Khuhawar, M. Y., et al., J Chromatogr B Biomed Sci Appl 723 (1-2),
17-24 (1999)). Oral polyamine levels are also affected by
periodontitis and gum healing (Silwood, C. J., et al., J Dent Res
81 (6), 422-427 (2002)).
[0105] These results revealed that levels of ornithine and
putrescine in the saliva of oral cancer patients were broadly
higher than healthy controls. Although it was known that
quantitative levels of polyamines were associated with regulating
tumour growth and also periodontitis status, our results indicates
that salivary polyamines are affected depending on cancer types and
periodontitis, prominently higher levels in oral cancers than
others.
[0106] In addition the polyamine, tryptophan (Carlin, J. M. et al.
Experientia 45 (6), 535-541 (1989)), which was found higher in oral
cancer, was previously identified as a marker for tumor
development. As indirect correction among the detected peaks and
human cancer, the repeat peptide of Pro-Pro-Gly, which showed
higher levels in breast cancer, is known as an inhibitor of matrix
metalloproteinase 2 (MMP-2, gelatinase A) which plays an important
role in tumor invasion or metastatis (Jani, M. et al., Biochimie 87
(3-4), 385-392 (2005)). The expression levels of amino acid
transporters ACST2 and LAT1 are elevated in primary human cancers,
which cancer cells optimize their metabolic pathways by activating
the exchange of amino acids between extra and intra cellular.
Peptide and acid are derived from various sources, e.g. fragmented
proteins, and the saliva metabolome profiles comprised of these
compounds might have been the integrated results.
[0107] The identified polyamine containing metabolites ornithine,
putrescine, and spermidine are all metabolites found in the urea
cycle of hepatocyte cells. Putrescine is derived from ornithine by
ornithine decarboxylase (ODC) and spermidine is derived from
putrescine by spermidine synthase. Polyamines are known to play an
important role in cell growth and proliferation (Gerner, E. W. and
Meyskens, F. L., Jr., Nat Rev Cancer, 4(10):781-92 (2004); Takes,
R. P., Oral Oncol, 40(7):656-67 (2004); Tabor, C. W. and Tabor, H.,
Annu Rev Biochem, 53:749-90 (1984); Seiler, N., Curr Drug Targets,
4(7):565-85 (2003); Casero, R. A., Jr. and Marton, L. J., Nat Rev
Drug Discov, 6(5):373-90 (2007)). Inhibition of the polyamine
synthesis pathway has been previously linked to tumor growth in
oral cancers (Dimery, I. W. et al., Am J Surg, 154(4):429-33
(1987)). A number of anti-cancer polyamine complexes (including
biogenic amines), with platinum(II) and palladium(II), have been
widely used in chemotherapy (Lomozik, L. et al., Coordination
Chemistry Reviews, 249(21-22):2335-2350 (2005)).
Example 2
[0108] Several factors, including human papilloma virus (HPV),
race, smoking, and alcohol dependence, were previously known as
risk factors for the development of OSCC and oropharyngeal cancer
(Kademani, D., Mayo Clin Proc, 82(7):878-87 (2007); Pintos, J. et
al., Oral Oncol, 44(3): 242-50 (2008); D'Souza, G. et al., N Engl J
Med, 356(19):1944-56 (2007)). Given the increased prevalence of
oral cancers among older individuals, it has been speculated that
age-related reductions in protective salivary antioxidant
mechanisms and/or an age-related increases in exposure to oral
carcinogens that cause DNA damage may play a role in the increased
frequency of oral cancer (Hershkovich, O. et al., J Gerontol A Biol
Sci Med Sci, 62(4):361-66 (2007)).
[0109] Age-related differences have been reported in a
transcriptome study of the salivary gland (Srivastava et al.
Archives of Oral Biology, 53 (11): 1058-1070 (2008)). It has been
reported that other methods commonly used for standardization of
metabolites in biofluid yield different statistical results
(Schnackenberg et al. BMC Bioinformatics, 8: S3 (2007)); therefore,
consistent decreases or increases in levels of metabolites among
subjects with correlated clinical parameters should be accounted
for. In the control subjects and patients with pancreatic cancer,
there was a positive correlation between metabolites and age,
whereas there was not in patients with oral or breast cancer or
periodontal diseases. Accordingly, it is unlikely that age is
correlated with the concentrations of salivary metabolites.
[0110] Relative areas of metabolite peaks were then compared
between males and females in both the control cohort and the oral
cancer cohort. In the oral cancer cohorts, piperidine, serine, and
threonine levels were significantly different (p<0.05;
Mann-Whitney) between males and females. In the healthy cohorts,
tyrosine and the unidentified metabolite with a mass to charge
ratio of 214.444 m/z was significantly different. The PCA analysis
of 57 metabolite markers based on gender (FIG. 4) and race or
ethnic groups (FIG. 5) also showed no clear separation, which might
indicate the deviation of individual samples in available clinical
parameters were also not significant.
[0111] Although, age had the greatest influence on metabolite
concentrations in healthy individuals, metabolite concentrations in
patients with oral cancers did not appear to be influenced by the
individual's age. These data indicate that oral cancer status has a
greater influence on metabolite concentration than does either age
or sex.
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[0144] It is understood that the examples and embodiments described
herein are for illustrative purposes only and that various
modifications or changes in light thereof will be suggested to
persons skilled in the art and are to included within the spirit
and purview of this application and scope of the appended claims.
All publications, patents, and patent applications cited herein are
hereby incorporated by reference in their entirety for all
purposes.
* * * * *
References